Laboratory scheduling method and collaborative system
By obtaining and evaluating experimental requirements and employee data in the laboratory scheduling collaborative system and setting up constraint planning models to optimize scheduling solutions, the problems of scheduling complexity and inefficiency in the existing technology are solved, and efficient, flexible and Humanized experimental staff scheduling.
Patent Information
- Application Number
- CN202411507900.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-28
- Publication Date
- 2025-02-18
Smart Images
Figure CN119477247A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of task scheduling, and in particular to a laboratory scheduling method and a collaborative system. Background Art
[0002] Experimenter scheduling is the core link in laboratory personnel management, which is related to the smooth progress of experiments, experimental efficiency, and the workload of experimenters. However, in many laboratories, personnel scheduling is still a complex and tedious task, facing many difficulties and challenges.
[0003] A complete animal experiment often involves the collaboration of multiple departments, each of which has its own special work arrangements. At the same time, in each experimental link, each experimenter has his or her own expertise and skills, and each experiment will also have specific requirements. For example, some experiments may require specific equipment operation skills, while other experiments may require expertise in a specific field. In addition, the experimenter's working hours, vacation time, workload, etc. also need to be taken into account. Therefore, the scheduling of experimenters needs to take into account various factors to ensure that each experiment can proceed smoothly.
[0004] Currently, laboratory scheduling is complex and experimental scheduling is a highly complex problem. It needs to take into account multiple factors such as the needs of the experiment, the skills and schedule of the experimenters, and the use of equipment in the laboratory. In addition, the needs of the experiment and the availability of personnel may change over time, which increases the difficulty of scheduling.
[0005] In the prior art, manual scheduling is often used, and personnel scheduling still relies mainly on manual work. This is not only inefficient, but also prone to errors. In addition, manual scheduling consumes a lot of time and energy, and it is difficult to handle complex scheduling requirements and emergencies. At the same time, manual scheduling is also difficult to maximize the efficiency of personnel time.
[0006] Although there are some laboratory staff scheduling tools on the market, which often cannot meet the needs of all laboratories, these tools have the following limitations: 1. Flexibility: Most tools are too simple and inflexible to handle complex animal experiment scheduling requirements. For example, when experimental requirements change or experimental staff cannot work as originally planned, these tools may not provide effective solutions. 2. Optimization capability: Experimental staff scheduling is essentially an optimization problem, which requires finding the optimal staff allocation solution under the premise of meeting various constraints. However, many existing scheduling tools have limited optimization capabilities and cannot find the true optimal solution. For example, they may not take into account the personal preferences of experimental staff, or they cannot balance various conflicting optimization goals, such as the conflict between improving experimental efficiency and reducing staff work pressure. 3. Degree of intelligence: With the development of artificial intelligence and machine learning technology, intelligent scheduling has become possible. By learning historical scheduling data, machines can predict experimental needs, automatically adjust scheduling strategies, and even proactively discover and solve scheduling problems. However, the degree of intelligence of many existing scheduling tools needs to be improved. 4. Practicality and coordination issues: In an enterprise, the scheduling of experimental personnel often involves different departments and organizations. The existing scheduling scheme requires frequent offline communication and data transmission, which is inefficient and has poor real-time performance, and cannot respond quickly and accurately to changes in experimental needs.
[0007] Therefore, this field needs to study a laboratory scheduling method and collaborative system that can propose smarter, more flexible, and more powerful scheduling solutions to cope with various emergencies and actual changes, improve the overall work efficiency of experimenters, ensure the work enthusiasm of experimenters, and at the same time ensure the success rate of the experiment. Summary of the invention
[0008] In order to solve the above technical problems, the present invention provides a laboratory scheduling method, which is based on a laboratory scheduling collaborative system and includes the following steps:
[0009] Obtaining experiment requirements, including experiment type, duration, required skills, required resources, and experiment priority;
[0010] Evaluate experimental requirements, classify or categorize the experimental requirements, and generate experimental requirement data packages;
[0011] Obtaining employee data, including employee work hours, employee skill level, employee work experience, and employee scheduling preferences;
[0012] Assess employee capabilities, grade or classify said employee data, and generate an employee capability data package;
[0013] A constraint programming model is set up, constraint programming is performed according to the experimental demand data package and the employee capability data package, the optimization target is solved, and an optimized scheduling plan is generated.
[0014] Optionally, the optimization objectives include maximizing the number of experiments completed, balancing the workload of employees and the degree of experiment completion, and the satisfaction of employees with their scheduling preferences.
[0015] Optionally, the evaluation experiment requirements include:
[0016] The skills required for the experiment were graded from 1 to 5, with 1 being the lowest level and 5 being the highest level;
[0017] Classify the resources required for the experiment, pair the experiment type with the resources required for the experiment, and match the required resources to the corresponding experiment type;
[0018] Experiment priorities are graded from 1 to 3, with 1 being low, 2 being medium, and 3 being high.
[0019] Optionally, assess employee capabilities, including:
[0020] Record and grade the employee's skill level, and divide it into 1 to 5 levels according to the employee's skill operation times and experimental results, with 1 being the lowest level and 5 being the highest level. The employee's skill level corresponds one-to-one with the level of skill required for the experiment;
[0021] The employee's work experience is graded from 1 to 5, with 1 being the lowest level and 5 being the highest level, and the employee's work experience corresponds to the employee's skill level;
[0022] Employee scheduling preferences are classified, and the employee scheduling preferences correspond to the experiment types one by one.
[0023] Optionally, the experiment requirement data packet stores all the experiment requirement data of the current period, and the current period refers to the time period from the last upload of data to the current moment.
[0024] Optionally, the employee capability data packet stores all employee data of the current period, where the current period refers to a time period from the last data upload to the current moment.
[0025] Optionally, the setting of the constraint programming model, performing constraint programming according to the experimental requirement data package and the employee capability data package, solving the optimization target, and generating the optimized scheduling plan specifically includes:
[0026] Define decision variables, objective functions and constraints, input the functions and constraints into the solver, call the solver to solve, and output the optimized scheduling plan.
[0027] Optionally, the decision variable is a two-dimensional matrix, and each element in the two-dimensional matrix represents the number of times a specific employee is assigned to a specific task.
[0028] Optionally, the optimization goals of the objective function include maximizing the number of experiments completed, balancing the workload of employees and the degree of experiment completion, and the satisfaction of employees with their scheduling preferences.
[0029] Optionally, the constraint conditions include:
[0030] The skills and resources required for each experiment cannot exceed the sum of the skill level and work experience level of the employees assigned to the experiment;
[0031] The duration of each experiment cannot exceed the sum of the working hours of the employees assigned to the experiment;
[0032] On the premise of meeting the above two conditions, experimental tasks are allocated based on employees' scheduling preferences.
[0033] Optionally, the shift scheduling plan includes experimental tasks for each employee in each time period.
[0034] A second aspect of the present invention provides a laboratory scheduling collaboration system, the system is used to implement the above method, the system comprises:
[0035] An experiment requirement input module, used to input experiment requirements, wherein the experiment requirements include experiment type, duration, required skills, required resources and experiment priority;
[0036] The experimental demand assessment module is used to disassemble the experimental demand and classify or categorize the experimental demand to generate an experimental demand data package; the disassembly refers to specifying the order of tasks, the collaborative process of tasks, the number and skill requirements of personnel, and the mode of task execution for the upstream and downstream tasks of the experiment;
[0037] Experimental requirement data storage module, used to store experimental requirement data packets;
[0038] Employee data entry module: used to enter employee data, including employee working hours, employee skill level, employee work experience and employee scheduling preferences;
[0039] An employee capability assessment module, used to grade or classify the employee data and generate an employee capability data package;
[0040] An employee capability data storage module is used to store employee capability data packets;
[0041] The data maintenance module is used to maintain the experimental demand data and the employee capability data, and update the experimental demand data package and the employee capability data package according to the changes of the experimental demand data and the employee capability data;
[0042] A scheduling module is used to perform constraint programming according to the experimental demand data package and the employee capability data package, solve the optimization objectives, and generate an optimized scheduling plan. The optimization objectives include maximizing the number of completed experiments, balancing the employee's workload and the degree of experimental completion, and the employee's scheduling preference satisfaction;
[0043] The shift scheduling storage module is used to store the optimized shift scheduling plan;
[0044] The query module is used to query the current optimized scheduling plan.
[0045] According to a third aspect of the present invention, there is provided an intelligent device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.
[0046] A fourth aspect of the present invention provides a storage medium having a program stored thereon, and when the program is executed by a processor, it is used to implement the method described in the first aspect.
[0047] The sample pretreatment method of the present invention has the following beneficial effects:
[0048] 1. The laboratory scheduling method provided by the present invention can output an optimized scheduling plan by inputting and analyzing experimental data and employee data, reasonably allocating experimental needs and employee capabilities, while taking into account the scheduling preferences of employees. The experimental scheduling is efficient, flexible and humane.
[0049] 2. The laboratory scheduling collaborative system provided by the present invention can be installed in a server terminal and, in conjunction with an intelligent mobile terminal, can provide employees with a platform for real-time online query and updating of scheduling plans. It can not only display the currently generated optimized scheduling plan, but also update and maintain the corresponding experimental demand data and employee capability data based on the employee's input data to generate an updated optimized scheduling plan and quickly adjust the scheduling plan to adapt to new needs. It has high collaboration and good practicality.
[0050] 3. Using the laboratory scheduling method and collaborative system provided by the present invention, the intelligent scheduling algorithm and the online collaborative system cooperate with each other to effectively integrate various resources and needs, and quickly generate a flexible and compact personnel scheduling plan, which not only overcomes the limitations of traditional manual scheduling methods, such as inefficiency, inflexibility and fairness issues, but also greatly improves the efficiency and accuracy of laboratory personnel scheduling. The laboratory scheduling method and collaborative system provided by the present invention jointly realize efficient, flexible and fair personnel scheduling. The optimized scheduling system combined with the real-time online collaborative system is more conducive to the display, execution and feedback of the scheduling plan, further improving the fluency and overall efficiency of experiments and work. BRIEF DESCRIPTION OF THE DRAWINGS
[0051] Figure 1 It is a flowchart for reflecting the laboratory scheduling method in an embodiment of the present invention;
[0052] Figure 2 It is a structural block diagram for reflecting the laboratory scheduling collaborative system in an embodiment of the present invention. DETAILED DESCRIPTION
[0053] The technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0054] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.
[0055] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, it can also be the internal connection of two components, it can be a wireless connection, or it can be a wired connection. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.
[0056] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.
[0057] Embodiment 1
[0058] refer to Figure 1 , discloses a laboratory scheduling method, based on a laboratory scheduling collaborative system. In implementation, the laboratory scheduling collaborative system is installed in a server terminal, or data interaction is realized in the form of multiple intelligent mobile terminals. The method includes the following steps:
[0059] S10. Obtaining experimental requirements, where the experimental requirements include the type of experiment, duration, required skills, required resources, and experimental priority;
[0060] S20, evaluating the experimental requirements, grading or classifying the experimental requirements, and generating an experimental requirements data package;
[0061] S30, acquiring employee data, wherein the employee data includes employee working hours, employee skill level, employee work experience, and employee scheduling preference;
[0062] S40, evaluating employee capabilities, grading or classifying the employee data, and generating an employee capability data package;
[0063] S50, setting a constraint programming model, performing constraint programming according to the experimental demand data package and the employee capability data package, solving the optimization goal, and generating an optimized scheduling plan, wherein the optimization goal includes maximizing the number of completed experiments, balancing the employee's workload and the degree of experimental completion, and the employee's satisfaction with the scheduling preference.
[0064] Based on the above method, a laboratory scheduling collaborative system is provided. Figure 2 , the laboratory scheduling collaborative system includes:
[0065] An experiment requirement input module, used to input experiment requirements, wherein the experiment requirements include experiment type, duration, required skills, required resources and experiment priority;
[0066] The experimental demand assessment module is used to disassemble the experimental demand and classify or categorize the experimental demand to generate an experimental demand data package; the disassembly refers to specifying the order of tasks, the collaborative process of tasks, the number and skill requirements of personnel, and the mode of task execution for the upstream and downstream tasks of the experiment;
[0067] Experimental requirement data storage module, used to store experimental requirement data packets;
[0068] Employee data entry module: used to enter employee data, including employee working hours, employee skill level, employee work experience and employee scheduling preferences;
[0069] An employee capability assessment module, used to grade or classify the employee data and generate an employee capability data package;
[0070] An employee capability data storage module is used to store employee capability data packets;
[0071] The data maintenance module is used to maintain the experimental demand data and the employee capability data, and update the experimental demand data package and the employee capability data package according to the changes of the experimental demand data and the employee capability data;
[0072] During implementation, when a temporary situation occurs where an employee is unable to report to work or a temporary change occurs in the experimental requirements, the data maintenance module can synchronize employee data with experimental requirement data in real time, update the experimental requirement data package and employee capability data package, and facilitate the scheduling module to generate a new optimized scheduling plan and respond to emergencies in a timely manner.
[0073] In implementation, the data maintenance module can input and output the status of laboratory and equipment resources, including available time and maintenance time, and transmit the status information to the experimental demand data package to improve the flexibility of scheduling.
[0074] A scheduling module is used to perform constraint programming according to the experimental demand data package and the employee capability data package, solve the optimization objectives, and generate an optimized scheduling plan. The optimization objectives include maximizing the number of completed experiments, balancing the employee's workload and the degree of experimental completion, and the employee's scheduling preference satisfaction;
[0075] The shift scheduling storage module is used to store the optimized shift scheduling plan;
[0076] The query module is used to query the current optimized scheduling plan. During implementation, employees can query the current optimized scheduling plan in real time through smart mobile terminals, understand their own work tasks and time arrangements, and understand the scheduling of the entire laboratory, which helps employees prepare for work and improve work efficiency.
[0077] In implementation, the duration of the experiment can be obtained based on historical data analysis and used as the input of the algorithm experiment time.
[0078] In implementation, different experiments require different skills, and the requirements for the same skills are also different. The experimental skills can be graded based on business analysis or grading standards that are well known in the field.
[0079] Optionally, the evaluation experiment requirements include:
[0080] The skills required for the experiment were graded from 1 to 5, with 1 being the lowest level and 5 being the highest level;
[0081] Classify the resources required for the experiment, pair the experiment type with the resources required for the experiment, and match the required resources to the corresponding experiment type;
[0082] Experiment priorities are graded from 1 to 3, with 1 being low, 2 being medium, and 3 being high.
[0083] During implementation, when resources are tight, high-priority experiments should be scheduled first to ensure maximum efficiency.
[0084] During implementation, employees' capabilities and available time should also be quantitatively assessed, which can be obtained through questionnaires, interviews, and historical performance data analysis.
[0085] Optionally, assess employee capabilities, including:
[0086] Record and grade the employee's skill level, and divide it into 1 to 5 levels according to the employee's skill operation times and experimental results, with 1 being the lowest level and 5 being the highest level. The employee's skill level corresponds one-to-one with the level of skill required for the experiment;
[0087] The employee's work experience is graded from 1 to 5, with 1 being the lowest level and 5 being the highest level, and the employee's work experience corresponds to the employee's skill level;
[0088] Employee scheduling preferences are classified, and the employee scheduling preferences correspond to the experiment types one by one.
[0089] Optionally, the experiment requirement data packet stores all the experiment requirement data of the current period, and the current period refers to the time period from the last upload of data to the current moment.
[0090] Optionally, the employee capability data packet stores all employee data of the current period, where the current period refers to a time period from the last data upload to the current moment.
[0091] In implementation, the above evaluation results can be matched. Optionally, for experiments requiring higher-level skills, employees with corresponding skill levels should be matched, and for experiments with higher priorities, employees with corresponding work experience levels should be matched.
[0092] Optionally, the setting of the constraint programming model, performing constraint programming according to the experimental requirement data package and the employee capability data package, solving the optimization target, and generating the optimized scheduling plan specifically includes:
[0093] Define decision variables, objective functions and constraints, input the functions and constraints into the solver, call the solver to solve, and output the optimized scheduling plan.
[0094] Optionally, the decision variable is a two-dimensional matrix, and each element in the two-dimensional matrix indicates whether a specific employee is assigned to a specific task. ij , there are only two values, 1 and 0, X ij =1 means the i-th employee is assigned to the j-th task, X ij =0 means that the i-th employee is not assigned to the j-th task.
[0095] Optionally, the optimization goals of the objective function include maximizing the number of experiments completed, balancing the workload of employees and the degree of experiment completion, and the satisfaction of employees with their scheduling preferences.
[0096] Optionally, the objective function is defined as ∑(number of completed tasks)-λ∑(employee workload), where λ is a parameter for adjusting the balance. In some embodiments, it can be defined as ∑ ij X ij , represents the sum of the number of completed tasks, that is, the sum of all decision variables, where the premise is that a task can be completed by at most one employee.
[0097] Optionally, the constraint conditions include:
[0098] The skills and resources required for each experiment cannot exceed the sum of the skill level and work experience level of the employees assigned to the experiment; in some embodiments, for example, a j X ij ≤ b i , indicating that the required skill level of the j-th task cannot exceed the skill level of the corresponding i-th employee assigned to do the task.
[0099] The duration of each experiment cannot exceed the sum of the working hours of the employees assigned to the experiment;
[0100] In the implementation, employees' scheduling preferences can be used as soft constraints to meet employees' preferences as much as possible to improve job satisfaction and work efficiency. Optionally, on the premise of meeting the above two conditions, experimental tasks are allocated based on employees' scheduling preferences.
[0101] Optionally, the scheduling plan includes the experimental tasks for each employee in each time period. In implementation, the specific optimization scheduling plan depends on the matching results of the experimental requirements and the employee data. Optionally, if the number of experiments and employees is equal, a solution may be obtained in which one experiment is assigned to each employee; if the number of experiments is greater than the number of employees, a solution may be obtained in which multiple experiments are assigned to each employee. Optionally, considering the workload of employees, a solution that balances the workload of employees may be obtained. The final scheduling plan should clearly indicate the experimental tasks of each employee in each time period.
[0102] Optionally, the present application also provides an intelligent device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the method provided in the above method embodiment when executing the computer program.
[0103] Optionally, the present application also provides a computer-readable storage medium, which stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the method provided by the above method embodiment.
[0104] In summary, the above embodiments are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.
Claims
1. A laboratory scheduling method, characterized in that: The method is based on a laboratory scheduling collaborative system and comprises the following steps: Obtaining experiment requirements, including experiment type, duration, required skills, required resources, and experiment priority; Evaluate experimental requirements, classify or categorize the experimental requirements, and generate experimental requirement data packages; Obtaining employee data, including employee work hours, employee skill level, employee work experience, and employee scheduling preferences; Assess employee capabilities, grade or classify said employee data, and generate an employee capability data package; A constraint programming model is set up, constraint programming is performed according to the experimental demand data package and the employee capability data package, the optimization target is solved, and an optimized scheduling plan is generated.
2. The laboratory scheduling method according to claim 1, characterized in that: The optimization objectives include maximizing the number of completed experiments, balancing the workload of employees and the degree of completion of experiments, and the satisfaction of employees with their scheduling preferences.
3. The laboratory scheduling method according to claim 1, characterized in that: The evaluation experiment requirements include: The skills required for the experiment were graded from 1 to 5, with 1 being the lowest level and 5 being the highest level; Classify the resources required for the experiment, pair the experiment type with the resources required for the experiment, and match the required resources to the corresponding experiment type; Experiment priorities are graded from 1 to 3, with 1 being low, 2 being medium, and 3 being high.
4. The laboratory scheduling method according to claim 1, characterized in that: Assess employee capabilities, including: Record and grade the employee's skill level, and divide it into 1 to 5 levels according to the employee's skill operation times and experimental results, with 1 being the lowest level and 5 being the highest level. The employee's skill level corresponds one-to-one with the level of skill required for the experiment; The employee's work experience is graded from 1 to 5, with 1 being the lowest level and 5 being the highest level, and the employee's work experience corresponds to the employee's skill level; Employee scheduling preferences are classified, and the employee scheduling preferences correspond to the experiment types one by one.
5. The laboratory scheduling method according to claim 1, characterized in that: The experiment requirement data package stores all the experiment requirement data of the current period, and the current period refers to the time period from the last upload of data to the current moment.
6. The laboratory scheduling method according to claim 1, characterized in that: The employee capability data package stores all employee data of the current period, and the current period refers to the time period from the last data upload to the current moment.
7. The laboratory scheduling method according to claim 1, characterized in that: The setting of the constraint programming model, performing constraint programming according to the experimental demand data package and the employee capability data package, solving the optimization target, and generating an optimized scheduling plan specifically includes: Define decision variables, objective functions and constraints, input the functions and constraints into the solver, call the solver to solve, and output the optimized scheduling plan.
8. The laboratory scheduling method according to claim 7, characterized in that: The decision variable is a two-dimensional matrix, and each element in the two-dimensional matrix represents the number of times a specific employee is assigned to a specific task.
9. The laboratory scheduling method according to claim 7, characterized in that: The optimization objectives of the objective function include maximizing the number of completed experiments, balancing the workload of employees and the degree of completion of experiments, and the satisfaction of employees with their scheduling preferences.
10. The laboratory scheduling method according to claim 7, characterized in that: The constraints include: The skills and resources required for each experiment cannot exceed the sum of the skill level and work experience level of the employees assigned to the experiment; The duration of each experiment cannot exceed the sum of the working hours of the employees assigned to the experiment; On the premise of meeting the above two conditions, experimental tasks are allocated based on employees' scheduling preferences.
11. The laboratory scheduling method according to claim 1, characterized in that: The shift scheduling plan includes experimental tasks for each employee in each time period.
12. A laboratory scheduling collaborative system, characterized in that: The system is used to implement the method according to any one of claims 1 to 10, and the system comprises: An experiment requirement input module, used to input experiment requirements, wherein the experiment requirements include experiment type, duration, required skills, required resources and experiment priority; The experimental demand assessment module is used to disassemble the experimental demand and classify or categorize the experimental demand to generate an experimental demand data package; the disassembly refers to specifying the order of tasks, the collaborative process of tasks, the number and skill requirements of personnel, and the mode of task execution for the upstream and downstream tasks of the experiment; Experimental requirement data storage module, used to store experimental requirement data packets; Employee data entry module: used to enter employee data, including employee working hours, employee skill level, employee work experience and employee scheduling preferences; An employee capability assessment module, used to grade or classify the employee data and generate an employee capability data package; An employee capability data storage module is used to store employee capability data packets; The data maintenance module is used to maintain the experimental demand data and the employee capability data, and update the experimental demand data package and the employee capability data package according to the changes of the experimental demand data and the employee capability data; A scheduling module is used to perform constraint programming according to the experimental demand data package and the employee capability data package, solve the optimization objectives, and generate an optimized scheduling plan. The optimization objectives include maximizing the number of completed experiments, balancing the employee's workload and the degree of experimental completion, and the employee's scheduling preference satisfaction; The shift scheduling storage module is used to store the optimized shift scheduling plan; The query module is used to query the current optimized scheduling plan.
13. An intelligent device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 11 is implemented.
14. A storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, it is used to implement the method according to any one of claims 1 to 11.